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BDCC
Special Issues
Emerging Trends and Applications of Big Data in Robotic Systems
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Emerging Trends and Applications of Big Data in Robotic Systems

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 926

Special Issue Editors

Dr. Praneel Chand

E-Mail Website
Guest Editor
Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
Interests: machine learning; AI; computer/machine vision; robotics; automation; mechatronics
Dr. Mansour Assaf

E-Mail Website
Guest Editor
School of Information Technology, Engineering, Mathematics, and Physics, The University of the South Pacific, Suva 1168, Fiji
Interests: fault-tolerant computing; embedded applications; sensor technologies
Dr. Mohammad Dabbagh

E-Mail Website
Guest Editor
School of Information Technology and Engineering, Melbourne Institute of Technology, Sydney, NSW 2000, Australia
Interests: empirical software engineering; block chain; big data
Dr. Nandini Sidnal

E-Mail Website
Guest Editor
Centre for Artificial Intelligence Research and Optimization, Faculty of Design and Creative Technologies, Torrens University Australia, Ultimo Campus, Sydney, NSW 2000, Australia
Interests: AI (artificial intelligence); cognitive computing; enhancing security using block chain technology; semantic analytics; big data analytics; enhancing the learning curve of students in the era of bots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data were associated with robotics long before big data gained popularity. Robotics always involves data, from sensing an environment to planning actions and executing tasks. Diverse sensing modalities can produce large amounts of data without specifically being labelled as “big data”. It is crucial to make accurate and meaningful decisions using large volumes of data so that robotic systems can function appropriately in the real world. Big data processing, analytics, models, algorithms, and architectures all play a vital role in  this.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • data-driven robotics
  • machine learning applications in robotics
  • data mining in robotics
  • simultaneous localisation and mapping (SLAM)
  • human-machine/robot interaction
  • multi-robot systems
  • collaborative robots (cobots)
  • cloud robotics
  • other related applications of big data robotics. 

We look forward to receiving your contributions. 

Dr. Praneel Chand
Dr. Mansour Assaf
Dr. Mohammad Dabbagh
Dr. Nandini Sidnal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • data mining
  • machine learning
  • deep learning
  • robotics
  • human–machine interaction
  • analytics

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Published Papers (1 paper)

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30 pages, 9597 KiB  
Article
PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines
by Praveen Kumar Sekharamantry, Marada Srinivasa Rao, Yarramalle Srinivas and Archana Uriti
Big Data Cogn. Comput. 2024, 8(12), 176; https://doi.org/10.3390/bdcc8120176 - 1 Dec 2024
Viewed by 546
Abstract
In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due [...] Read more.
In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due to the changes in the effects of image light, stance, and orientation. Further, it is difficult to identify the medicinal plants due to factors like variations in leaf shape with age and changing leaf color in response to varying weather conditions. The proposed work uses machine learning techniques and deep neural networks to choose appropriate leaf features to determine if the leaf is a medicinal or non-medicinal plant. This study presents a neural network design based on PSR-LeafNet (PSR-LN). PSR-LeafNet is a single network that combines the P-Net, S-Net, and R-Net, all intended for leaf feature extraction using the minimum redundancy maximum relevance (MRMR) approach. The PSR-LN helps obtain the shape features, color features, venation of the leaf, and textural features. A support vector machine (SVM) is applied to the output achieved from the PSR network, which helps classify the name of the plant. The model design is named PSR-LN-SVM. The advantage of the designed model is that it suits more considerable dataset processing and provides better results than traditional neural network models. The methodology utilized in the work achieves an accuracy of 97.12% for the MalayaKew dataset, 98.10% for the IMP dataset, and 95.88% for the Flavia dataset. The proposed models surpass all the existing models, having an improvement in accuracy. These outcomes demonstrate that the suggested method is successful in accurately recognizing the leaves of medicinal plants, paving the way for more advanced uses in plant taxonomy and medicine. Full article
(This article belongs to the Special Issue Emerging Trends and Applications of Big Data in Robotic Systems)
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